A variety of technologies exist for collecting and mining user activity data reflective of the actions and preferences of users of an electronic catalog. For example, it is known in the art to collectively analyze the activity data of a population of users to identify items that tend to be viewed, purchased, or otherwise selected in combination. Different types of item relationships may be detected by applying different similarity metrics to the activity data. For instance, a pair of items, A and B, may be identified as likely substitutes because a relatively large number of the users who viewed A also viewed B during the same browsing session. Items C and D, on the other hand, may be identified as complementary because a relatively large number of those who purchased C also purchased D.
The item relationships detected through this process may be exposed to users of the electronic catalog to assist users in identifying items of interest. For example, in some systems, when a user views a catalog item, the user is informed of other items that are commonly viewed (or purchased) by those who have viewed (or purchased) this item. Although this type of data assists the user in identifying a set of candidate items from which to make a selection (e.g., a set of consumer electronics products with similar features), it generally does not help the user discriminate between these candidate items. Thus, the user typically must rely solely on the descriptions of the candidate items, including any user ratings and reviews, in making a purchase decision.